IEEE Trans Image Process - Transferring visual prior for online object tracking.

Tópicos

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Resumo

Visual prior from generic real-world images can be learned and transferred for representing objects in a scene. Motivated by this, we propose an algorithm that transfers visual prior learned offline for online object tracking. From a collection of real-world images, we learn an overcomplete dictionary to represent visual prior. The prior knowledge of objects is generic, and the training image set does not necessarily contain any observation of the target object. During the tracking process, the learned visual prior is transferred to construct an object representation by sparse coding and multiscale max pooling. With this representation, a linear classifier is learned online to distinguish the target from the background and to account for the target and background appearance variations over time. Tracking is then carried out within a Bayesian inference framework, in which the learned classifier is used to construct the observation model and a particle filter is used to estimate the tracking result sequentially. Experiments on a variety of challenging sequences with comparisons to several state-of-the-art methods demonstrate that more robust object tracking can be achieved by transferring visual prior.

Resumo Limpo

visual prior generic realworld imag can learn transfer repres object scene motiv propos algorithm transfer visual prior learn offlin onlin object track collect realworld imag learn overcomplet dictionari repres visual prior prior knowledg object generic train imag set necessarili contain observ target object track process learn visual prior transfer construct object represent spars code multiscal max pool represent linear classifi learn onlin distinguish target background account target background appear variat time track carri within bayesian infer framework learn classifi use construct observ model particl filter use estim track result sequenti experi varieti challeng sequenc comparison sever stateoftheart method demonstr robust object track can achiev transfer visual prior

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